Imaging for Cancer Radiation Therapy

MEDN62681 Imaging for Cancer Radiation Therapy 15 Semester 1 Postgraduate Taught Level 6 Cynthia Eccles Unit coordinator Alan McWilliam Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 The unit will provide the students with the following knowledge and practical skills:<ul><li>The unit will present the role of imaging in cancer radiation therapy, covering CT, MRI, and PET.</li><li>Provide the understanding of the importance of imaging in radiotherapy and where it fits in the patient pathway, including why different image modalities provide complementary information and the requirements for different treatment sites.</li><li>CT, cone-beam CT, MRI and PET will be discussed, compared and contrasted, including aspects concerning hardware, acquisition, reconstruction, and applications.</li><li>Visits to clinical imaging suites will be arranged (depending on clinical workload).</li><li>Knowledge and experience of the DICOM standard, including practical experience in setting up a DICOM network with multiple nodes including an image viewer. Students will perform simple operations to query networked databases and send images to be viewed.</li><li>Image processing will be introduced and evaluated including: the definition of an image, visualisation, window and level, filtering, image interpolation, edge detection, rendering, transformations, thresholding.</li><li>Image registration will be described and appraised, discussing the workflow to explain transformation, interpolation, optimisation, and cost functions. Both rigid and non-rigid registration methodologies, and inter- and intra-modality will be presented.</li><li>Students will synthesis their knowledge and apply within the practical assessment. Working in Python, students will define and develop an image registration workflow, using a simplified example of 2-dimensional images.</li><li>Clinical use of imaging will be appraised, including image driven motion management, automated segmentation, and clinical segmentation accuracy.</li></ul> The unit will provide the students with the following knowledge and practical skills: <ul> <li> The unit will present the role of imaging in cancer radiation therapy, covering CT, MRI, and PET.</li> <li> Provide the understanding of the importance of imaging in radiotherapy and where it fits in the patient pathway, including why different image modalities provide complementary information and the requirements for different treatment sites.</li> <li> CT, cone-beam CT, MRI and PET will be discussed, compared and contrasted, including aspects concerning hardware, acquisition, reconstruction, and applications.</li> <li> Visits to clinical imaging suites will be arranged (depending on clinical workload).</li> <li> Knowledge and experience of the DICOM standard, including practical experience in setting up a DICOM network with multiple nodes including an image viewer. Students will perform simple operations to query networked databases and send images to be viewed.</li> <li> Image processing will be introduced and evaluated including: the definition of an image, visualisation, window and level, filtering, image interpolation, edge detection, rendering, transformations, thresholding.</li> <li> Image registration will be described and appraised, discussing the workflow to explain transformation, interpolation, optimisation, and cost functions. Both rigid and non-rigid registration methodologies, and inter- and intra-modality will be presented.</li> <li> Students will synthesis their knowledge and apply within the practical assessment. Working in Python, students will define and develop an image registration workflow, using a simplified example of 2-dimensional images.</li> <li> Clinical use of imaging will be appraised, including image driven motion management, automated segmentation, and clinical segmentation accuracy.</li> </ul> This unit aims to provide students with knowledge to appraise and evaluate the role of imaging within cancer radiation therapy. Students will be able to understand and analyse the processes of image acquisition, reconstruction and application of CT, cone-beam CT, MRI, and PET. Students will understand role of, and be able to apply knowledge of, image processing and image fusion.Students will be able to critically compare and contrast different imaging modalities for different clinical tasks. Students will be able to synthesis their knowledge to create a simple image registration pipeline using python. Students should/will be able to:<ul><li>Apprise and evaluate the role of imaging in cancer pathways.</li><li>Analyse the acquisition, reconstruction, and application of ionising and non-ionising imaging techniques.</li><li>Evaluate the role of image processing and image fusion within medical physics.</li></ul> Students should/will be able to:<ul><li>Critically compare and contrast the different imaging modalities and their role in medical physics.</li><li>Determine suitable image processing and registration approaches for different tasks.</li><li>Evaluate how imaging and image processing are used in optimising patient pathways.</li><li>Examine future applications and developments in medical imaging.</li></ul> Students should/will be able to:<ul><li>Synthesise knowledge gained to design and produce a working image fusion pipeline.</li><li>Compare and contrast imaging modalities for different tasks.</li></ul> Students should/will be able to:<ul><li>Apply analytical skills to systematically analysis evidence.</li><li>Technical: Practical experience in building image processing pipelines.</li></ul> The following learning and teaching processes will be utilised: Classroom based teaching, podcasts, interactive computer simulation practical sessions and paired programming, formative assessments, interactive group based discussion and tutorial sessions, on-line resources, independent study, facility tours and demonstrations. 0 Other 50% 2 Written assignment (inc essay) 50% Summative 1: 50%Create a python processing pipeline for image registration - Juypter notebook.Students will work in pairs, i.e., paired-programming.Summative 2: 50%A written assessment with the title ‘Describe the physics and clinical aspects of a new imaging application in radiotherapy.’ (1,500 words)  Class-based and individual feedback based on defined marking scheme will be shared with the students. N Lectures 35 0 115 Study hours: Lectures; Tutorials; Seminars; Workshops - 35 hoursIndependent study - 115 hours